Category: Analytics

This blog post was reviewed and updated May 2022, to include and comply with recently published Part 3 from this series. Many customers request detailed steps to set up federated single sign-on (SSO) using Microsoft Active Directory Federation Services (AD FS) for Amazon Redshift. In this two-part series, you will find detailed steps to achieve […]

This post explores how you can use AWS Lake Formation integration with Amazon EMR (still in beta) to implement fine-grained column-level access controls while using Spark in a Zeppelin Notebook. My previous post Extract Salesforce.com data using AWS Glue and analyzing with Amazon Athena showed you a simple use case for extracting any Salesforce object data using AWS Glue and Apache Spark, saving it to Amazon Simple Storage Service (Amazon S3), cataloging the data using the Data Catalog in Glue, and querying it using Amazon Athena.

This is a guest post by Kong Zhao, Solution Architect at NVIDIA Corporation This post shares how NVIDIA sped up RAPIDS XGBoost performance up to 4.5 times faster and reduced costs up to 5.4 times less by using Amazon EMR running Amazon Elastic Compute Cloud (Amazon EC2) G4 instances. Gradient boosting is a powerful machine […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. This is a guest post by Delhivery. In this post, we describe the steps Delhivery took to migrate from self-managed Apache Kafka running on Amazon Elastic Compute […]

What if you could control the access to your data lake centrally? Would it be more convenient to share specific data securely with internal and external customers? With AWS Lake Formation and its integration with Amazon EMR, you can easily perform these administrative tasks. This post goes through a use case and reviews the steps to control the data access and permissions of your existing data lake.

This post demonstrates how to develop a comprehensive, wave-based application migration methodology for a complex project to modernize a traditional MPP data warehouse with Amazon Redshift. It provides best practices and lessons learned by considering business priority, data dependency, workload profiles and existing service level agreements (SLAs).

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Amazon OpenSearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate OpenSearch in AWS at scale. It’s a widely popular service and different customers integrate it in their applications for different search […]

AWS is happy to announce the release of Amazon EMR Managed Scaling—a new feature that automatically resizes your cluster for best performance at the lowest possible cost. With EMR Managed Scaling you specify the minimum and maximum compute limits for your clusters and Amazon EMR automatically resizes them for best performance and resource utilization. EMR Managed Scaling continuously samples key metrics associated with the workloads running on clusters. EMR Managed Scaling is supported for Apache Spark, Apache Hive and YARN-based workloads on Amazon EMR versions 5.30.1 and above.

With Amazon Redshift Spectrum, you can query the data in your Amazon Simple Storage Service (Amazon S3) data lake using a central AWS Glue metastore from your Amazon Redshift cluster. This capability extends your petabyte-scale Amazon Redshift data warehouse to unbounded data storage limits, which allows you to scale to exabytes of data cost-effectively. Like Amazon EMR, you get the benefits of open data formats and inexpensive storage, and you can scale out to thousands of Redshift Spectrum nodes to pull data, filter, project, aggregate, group, and sort. Like Amazon Athena, Redshift Spectrum is serverless and there’s nothing to provision or manage. You only pay $5 for every 1 TB of data scanned. This post discusses how to configure Amazon Redshift security to enable fine grained access control using role chaining to achieve high-fidelity user-based permission management.

This post demonstrates how to extend the Lake Formation security model to QuickSight users and groups, which allows data lake administrators to manage data catalog resource permissions centrally from one console. As organizations embark on the journey to secure their data lakes with Lake Formation, having the ability to centrally manage fine-grained permissions for QuickSight authors can extend the data governance and enforcement of security controls at the data consumption (business intelligence) layer. You can enable these fine-grained permissions for QuickSight users and groups at the database, table, or column level, and they’re reflected in the Athena dataset in QuickSight.